Artificial Neural Network and Response Surface-Based Combined Approach to Optimize the Oil Content of Ocimum basilicum var. thyrsiflora (Thai Basil)

Ocimum basilicum var. thyrsiflora is valuable for its medicinal properties. The barriers to the commercialization of essential oil are the lack of requisite high oil-containing genotypes and variations in the quantity and quality of essential oils in different geographic areas. Thai basil’s essential oil content is significantly influenced by soil and environmental factors. To optimize and predict the essential oil yield of Thai basil in various agroclimatic regions, the current study was conducted. The 93 datasets used to construct the model were collected from samples taken across 10 different agroclimatic regions of Odisha. Climate variables, soil parameters, and oil content were used to train the Artificial Neural Network (ANN) model. The outcome showed that a multilayer feed-forward neural network with an R squared value of 0.95 was the most suitable model. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. Garson’s algorithm was used to discover the influential predictors. Soil potassium content was found to have a very strong influence on responses, followed by maximum relative humidity and average rainfall, respectively. The study reveals that by adjusting the changeable parameters for high commercial significance, the ANN-based prediction model with the response surface methodology technique is a new and promising way to estimate the oil yield at a new site and maximize the essential oil yield at a particular region. To our knowledge, this is the first report on an ANN-based prediction model for Ocimum basilicum var. thyrsiflora.


Introduction
Ocimum basilicum var. thyrsiflora (Thai basil) is an important industrial medicinal plant belonging to the Ocimum species of the Lamiaceae family, which can be used in both raw and processed forms in traditional medicine and the pharmaceutical industry [1]. Ocimum species grow well in saline and alkaline soils with a moderately acidic pH, moderate to heavy rainfall, normal humidity, and high temperatures. This plant is mainly grown in tropical Asia, Africa, and Central and South America, although it is especially popular in China, Japan, Turkey, and Iran [2]. Ocimum species are well recognized for their essential oil, which is responsible for condiment flavour and plant aroma [3]. Thai basil oil has a significant commercial value because of the presence of phenylpropanoids such asestragole, methyl eugenol, (E)-α-bergamotene, and their derivatives, and terpenoids such as monoterpene alcohol linalool, limonene, and terpinolene. The most prevalent volatile components identified in the aroma profile of dried Thai basil included estragol, methyl eugenol, and (E)-α-bergamotene [4]. Over the past few decades, extensive research has shown that Ocimum basilicum extracts have antimicrobials, antioxidants [3], antiviral properties [5], anti-inflammatory properties [6], hypolipidemic effects [7], anti-platelet aggregation, and antithrombotic, antiulcerogenic, and anticarcinogenic [8] activities. It can decrease blood levels of LDL cholesterol while raising blood levels of HDL cholesterol, hence reducing cardiovascular diseases [9,10]. The export database reveals that India exported 1553 Rs per Kg of basil essential oil during 2016-2017 [11].
The major limitation in the commercial production of Thai basil is the lack of staging of high essential oil-containing genotypes and variation in the oil content in different agroclimatic regions. Because the production of essential oils is primarily driven by environmental conditions, it would be impossible to identify the genetically superior Thai basil with a high essential oil concentration using a simple chemotype. According to Rawat et al., there is a wide range of variations in the essential oil concentration of Ocimum species in various agroclimatic areas of Uttarakhand [12]. To ascertain the link between biochemical content and environmental variables, many statistical techniques are used. Multiple linear regression (MLR) analysis and correlation are two common statistical procedures that can only be used to find linear associations and are ineffective when applied to non-linear data [13]. Artificial neural networks (ANN) are increasingly routinely used to build and map non-linear relationships between inputs and outputs due to their improved prediction accuracy. ANN modelling is used to simulate how the human brain functions [14]. It was selected because it can quickly pick up lessons from events without first running statistical analyses on the parameters [15]. The input layer, hidden layers, and output layer of neurons are the three main divisions of an ANN [16]. The input layers' neurons receive the input data, which is then adjusted before being sent to the hidden layer [17]. Each neuron in the layer below performs a linear combination of the data from the neurons in the input layer, adding weight values related to certain nodes to the result. The projected model is the outcome of the neurons in the hidden layer combining the linear data from the input layer with a transfer function (a specific non-linear function) [16]. The ANN model has been used to predict the bioactive content of the compounds podophyllotoxin from Podophyllum hexandrum [18]; hyperforin, hypericin, and pseudohypericin from Hypericum perforatum L. [13]; and bacoside A from Bacopa monnieri [19].
Therefore, it will be mandatory to analyze the soil parameters and climatic factors of different agroclimatic areas of Odisha for high essential oil content. An Artificial Network (ANN) model and a Response surface-based combined approach for essential oil content in Thai basil can be developed to predict the appropriate site and enhance the essential oil content at a particular site by managing the sensitive and variable factors.

Plant Materials and Sample Station
From June to October 2021, samples of Thai basil leaves were collected from 93 sites across 10 agroclimatic regions in several districts of Odisha, at varying altitudes ranging from 0.1 to 1204 m (Table 1). Three replicates of the leaf samples were taken from each site. The two duplicates were separated by 2 to 5 m. To get rid of the dust, the samples of freshly collected leaves were first washed with running tap water and then with distilled water. Following air drying at room temperature, samples of washed leaves were used to determine the essential oil contents. To analyze soil nutrients, triplicate soil samples were taken from each sampling site and brought to the lab. The documented monthly average data on environmental variables, such as rainfall, temperature, or humidity, were noted from each sampling site from June 2021 to October 2021.

Extraction of Essential Oil and Quantification
The air-dried leaves were crushed and powdered in a mortar and pestle before being hydrodistilled in a Clevenger-type apparatus made entirely out of glass. For later usage, the oil was dried with anhydrous sodium sulphate and stored in a sealed Eppendorf tube at 4 • C.

Quantitative Analysis of Soil
In triplicate, soil samples were taken from each sampling site within an agroclimatic region. A soil sample of approximately 250 g was taken and sieved through a 2-mm mesh. Nutrient analysis was performed using fine soil. Using the Systronics pH meter (Model MKVI), the pH of soil samples was identified in soil: water 1:2 ratio suspension after 30 min of equilibration with infrequent stirring.
Using Bray's No 1 technique, the total phosphorus content of soil samples was determined. The solution was made by extracting 2 g of soil in 40 mL of Bray's solution, which contains 0.03 NH 4 F and 0.025 N HCL. This mixture was then agitated vigorously for five minutes using a mechanical shaker and filtered through Whatman paper. A 25 mL flask was filled with a 0.5 mL aliquot. Ammonium molybdate solution (0.5 mL) and distilled water were added to bring the volume up to 25 mL. To make up the volume, diluted SnCl 2 (0.5 mL was diluted in 66 mL) was added. The concentration of phosphorus was measured using a spectrophotometer (Model: Systronics 166) set at 660 nm. The concentration was measured using a standard graph created by varying the phosphorus content. The available phosphorus in the soil samples was determined by extracting the soil using Olsen's reagent (0.5 M NaHCO 3 , pH 8.5). The phosphorous concentration was measured using a spectrophotometer (Model: Systronics 166) and the mechanism was ascorbic acid reduced to blue-colored sulphomolybdic acid in the sulphuric acid system at 882 nm [20]. Next, 5 g of the soil sample was placed in a 100 mL conical flask along with 25 mL of 1 N NH 4 OAc solution to determine the amount of potassium present in the soil. An automatic shaker was used to shake the mixture for 5 min, after which the filtrate's potassium level was determined using a flame photometer (Model: Systronics 128). The Walkley and Black Wet Digestion Technique were used to calculate the organic carbon content in the soil sample by chemical analysis [21].
The alkaline KMnO 4 method was used to calculate nitrogen content. Next, 20 g of the soil sample were placed in an 800 mL Kjeldahl flask together with 100 mL of a 0.32% KMnO 4 solution, 2.5% NaOH solution, and distilled water. In a 250 mL conical flask containing 20 mL of 2% boric acid and a mixed indicator, the distillation process was continued, and the result was collected in a receiver tube. The available nitrogen was then calculated by titrating the distillate in a burette against 0.02 N H 2 SO 4 to a pink colour endpoint [22].

Data Exploration
All computational work (model development, plot generation, etc.) was performed using R (R Core Team 2021, Vienna, Austria). The data set consists of 12 features and 93 instances. Out of the features, 11 are predictors. The predictors are phosphorous, nitrogen, potassium, the organic carbon content, pH of the soil, maximum and minimum relative humidity, average rainfall, maximum and minimum average temperature, and altitude. Thai basil oil content is the response. Standard deviations for all features were calculated using the mlbench [23] library (Appendix A).
The formula for standard deviation is provided below.
where x and − x are the value of each observation and mean of all observations, respectively. Pearson's correlation coefficient between features and data distribution in each feature were evaluated using the package psych [24]. Pearson's correlation coefficient was calculated using equation 2. The correlation values were provided in the panel plot in the form of numeric values as well as a correlation ellipse ( Figure 1).

The Pearson s Correlation coefficient
where x and y are the values of the two variables; − x and − y are the respective means.

Data Splitting
The dataset (total data) is divided into three sets: train, test, and validation with 70%, 20%, and 10% of data, respectively. The train set was used to develop the model by training. The model was evaluated using the test set. Finally, the model was validated using a validation set.

Artificial Neural Network Model Development
The Caret (classification and regression training) package [25] was used to develop the artificial neural network model. Data was scaled using minimum-maximum normalization. The train set was resampled with 15-fold cross-validation during training. A grid-tuning approach was used to ascertain the optimal number of layers and nodes within each layer. The logistic function was selected as the activation function. A learning rate of 0.02 was maintained. Error calculations were performed using the Sum of Squared Errors.
where y andŷ are the actual response and predicted response, respectively. Pearson's correlation coefficient between features and data distribution in each feature were evaluated using the package psych [24]. Pearson's correlation coefficient was calculated using equation 2. The correlation values were provided in the panel plot in the form of numeric values as well as a correlation ellipse ( Figure 1).

Model Evaluation and Selection
The final model was selected based on the values of root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination or R squared (R squared) values. The above evaluation metrics were calculated using the following formulae:

Variable Importance
The variable (different factors) importance of the response was evaluated using the Neural Net Tools [26] library. To determine the ranking importance of predictors, Olden's method was used. To assess the significance of the predictors, the method analyzes the raw connection weights between input-hidden nodes and hidden-output nodes [27]. They have also compared several methods viz. input perturbation, partial derivatives, etc., and concluded that the connection weight approach-i.e., Olden's method-is more reliable.

Partial Dependence Plots and Faceted Heatmap
Partial dependence plots (PDP) were generated to investigate the interaction of predictors with the response. These plots were generated using the PDP library. The use of linear plots is not suitable for explaining the complex relationship of different variables with the response. PDP plots are used to interpret the output of complex machine-learning models [28]. In this study, single-variable and multiple-variable PDPs are generated. Smoothing is applied using locally weighted regression (LOESS) in the case of single variable PDPs, which has popularity in the smoothing of scatterplots [29]. LOESS can perform well even if the response is a nonlinear function of the predictor [30]. The relationship of the response variable with two predictors is represented by a two-dimensional contour and three-dimensional PDPs.
Another type of plot called a faceted heatmap was used to identify the behavior of the response concerning precise value ranges of predictors. For this purpose, a lime [31] package was used.

Sensitivity Analysis
It is critical to identify the most important elements influencing essential oil content. Therefore, Garson's algorithm was used to discover the influential predictors. The link strengths between the nodes are calculated to assess the relative importance of each predictor.

Results and Discussion
The pair plots ( Figure 1) show four different data set representations through ellipse plots, scatter plots, correlation coefficients, and histograms. The trend lines represent the linear relationship among variables in scatter plots. The correlation ellipse characterizes the correlation strength. When the stretch is higher, the correlation coefficient is also higher. When the correlation coefficient value is between 0 to 0.1, 0.1 to 0.39, and 0.4 to 0.69, the correlation is negligible, weak, or moderate, respectively. Correlation is strong when the coefficient value is between 0.7 and 0.89, and very strong when the correlation coefficient value is between 0.9 and 1 [32]. From the plot, pH has a moderate correlation with variables viz. minimum relative humidity and minimum average temperature. Correlations of pH with remaining variables were found to be negligible to weak. Similarly, the correlation strengths among variables are represented in Figure 1. Only two variables, minimum relative humidity and minimum average temperature, have shown a strong correlation, i.e., 0.71. A correlation study among the predictors has significance in model evaluation. The machine learning algorithms are affected if any correlation exists among the predictors [33]. The data set shows multicollinearity among its variables. In such cases, artificial neural Plants 2023, 12, 1776 9 of 28 networks (ANN) are found to be suitable as the ANN models are least affected due to correlations among input variables [34,35].

Model Evaluation and Selection
The final model along with climatic data (Table 2), soil data (Table 3), different layers, nodes, and connection strengths are provided in Figure 2. Root mean square error (RMSE) and mean absolute error (MAE) were used as performance measures to select the best model. The data set has multicollinearity among variables, so the RMSE can be used as a performance measure [35]. The model that had the lowest RMSE and MAE was selected. The RMSE, mean absolute error (MAE), and the coefficient of determination (R-squared) values of the model for the train, test, and validation data sets are provided in Figure 3. The predictions and the actual responses for the train set are provided in Table 4. The RMSE, MAE, and R squared values for training were 0.00, 0.02, and 0.99, respectively ( Figure 3).            After training, the model was evaluated using the test data set. The RMSE, MAE, and R squared values for test set data are 0.04, 0.13, and 0.95, respectively ( Figure 3). Furthermore, the model was validated with a validation set. The model also performed well with RMSE, MAE, and R squared values of 0.09, 0.09, and 0.92, respectively. Tables 5 and 6 show the predictions and actual responses for test and validation data, respectively. As far as we know, this is the first study to represent the extent of the essential oil content prediction of Thai basil. For predicting the essential oil content of Thai basil, the Artificial Neural Network (ANN) is recommended as one of the most promising methods. This networking system provides new possible approaches for the study of bioactive compounds in other plants as well as in other environmental conditions. As a predictive approach for maximizing the operating parameters during the extraction of various natural products, the ANN has been proposed by various researchers [36][37][38][39].  ANN does not require any inference of previous data structure; thus, it gathers an advantage over other statistical modeling techniques. It may detect complicated interactions and non-linear correlation, and expose the unknown linkage between previously input parameters [40]. The four statistical quality measures such as coefficient of determination (R squared), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to develop the ANN model. When the coefficient value is 0.9-1, the correlation is very strong, and 0.7-0.89 indicates a strong correlation. The correlation is negligible when the correlation coefficient value is between 0-0.1; likewise, 0-1-0.39 and 0.4-0.69 indicate that the correlation is weak and moderate, respectively [32]. In model evaluation, the correlation study among predictors has significance. If any correlation is present between the predictors, then the machine learning algorithms are affected [33]. The ANN model developed in this study determined the strong predictive potential for essential oil content for Thai basil because it was measured by correlation coefficient (R squared) and root mean square error (RMSE). Less variation between the projected value and experimental value is indicated by the model's higher R squared value of 0.92 and lower RMSE value of 0.09. The ANN model is called stronger when the R squared value is closer to 1 and the RMSE value is lower. Hence, it is possible to conclude that the model developed for the prediction of the essential oil content of Thai basil is considerably accurate. Similar studies were published in which error values were lower with high predictive analysis of the ANN model [41][42][43].

Significant Predictor Identification
According to the model, soil potassium content was found to have a very strong influence on response, followed by maximum relative humidity and average rainfall, respectively (Table 7). Minimum relative humidity was found to have the least effect on oil content. The relative importance of all variables on output is provided in Figure 4. Garson's algorithm was used to discern influential predictors and remove insignificant predictors. In an ANN, the coefficient in a generalized linear model is partially equivalent to the weights that connect neurons. There are different weights connecting one predictor to the outcome, and the weights' combined influence shows how important each predictor is about the outcome variable. A large number of adjustable weights makes an ANN very flexible and nonlinear, but also creates difficulties in interpretation [44,45]. A single value ranging from 0 to 1 that illustrates the relative relevance of predictors is produced by combining and scaling all weights that are linked to a predictor. The Neural Networking Tools (Version 1.5.1) package in R can be used to determine the relative importance [17].

Effect of Individual Predictors on Essential Oil Content
Single variable PDPs are generated for all predictors and provided in Figure 5a-k. The multicollinearity among the features of the data set is shown in Figure 1. Most of the correlation among variables is weak to moderate. Minimum average temperature and minimum average relative humidity have shown a strong correlation ( Figure 6). In such cases, ANN models provide promising results and are free from any biases due to multicollinearity [34]. Therefore, PDPs generated through artificial neural network models are suitable to study the change in response to predictors. The variation of oil content with soil potassium content was provided in Figure 5e. The essential oil content was found to be higher with low soil potassium content and average rainfall (Figure 5h). However, the oil content increased with an increase in organic carbon content (Figure 5b), nitro-gen (Figure 5c), maximum average temperature (Figure 5i), maximum relative humidity (Figure 5f), etc. The oil content showed dramatic variation with variation in minimum average temperature (Figure 5j). Initially, the oil content was found to decrease with an increase in minimum average temperature. When the temperature is beyond 25 • C, the oil content gradually increased. Similarly, when the maximum relative humidity exceeded 85 (approx.), the oil yield also gradually increased (Figure 5f). Partial dependence plots (PDPs) were used to show how one or two variables affect the predictions of the model. In this study, a univariate partial dependence plot is displayed alongside each pairwise partial dependence plot in a matrix-style layout. An analyst can easily observe and identify the important pairs of variables that have a significant impact on the model using this partial dependence plot.
ranging from 0 to 1 that illustrates the relative relevance of predictors is produced by combining and scaling all weights that are linked to a predictor. The Neural Networking Tools (Version 1.5.1) package in R can be used to determine the relative importance [17].

Effect of Individual Predictors on Essential Oil Content
Single variable PDPs are generated for all predictors and provided in Figure 5a-k. The multicollinearity among the features of the data set is shown in Figure 1. Most of the correlation among variables is weak to moderate. Minimum average temperature and minimum average relative humidity have shown a strong correlation ( Figure 6). In such cases, ANN models provide promising results and are free from any biases due to multicollinearity [34]. Therefore, PDPs generated through artificial neural network models are suitable to study the change in response to predictors. The variation of oil content with

Mutual Effect of Two Predictors on Response
Partial dependence plots with two variables show the mutual contribution of the variables to the response. Such plots help identify the optimum range of predictor values for a maximum value of the response. Two-variable PDPs (Figures 7a-e and 8a-e) are generated for the top five important variables (soil potassium content, altitude, maximum relative humidity, maximum average temperature, and average rainfall) to understand the mutual influence of these variables on response. For each pair of predictors, two different types of PDPs are generated: a 2D contour plot and 3D partial dependence plot. In each plot, the colour scale on the right-hand side shows the colour as a measure of essential oil content. From these values, it is evident that a lower value of soil potassium content, i.e., from 0 to 250 kg/ha (approx.), and an altitude range of 400 m to 800 m is favorable for higher oil content (Figures 7a and 8a). Similarly, a low value of average rainfall, i.e., 4 mm was found to be favourable for essential oil content (Figures 7b and 8b). The essential oil content is found to be higher when the maximum average temperature value is higher than  (Figures 7c and 8c). From Figures 7d and 8d, it is evident that a higher maximum relative humidity value (i.e., beyond 95) is found to be favourable for Thai basil oil. temperature (Figure 5j). Initially, the oil content was found to decrease with an increase in minimum average temperature. When the temperature is beyond 25 °C, the oil content gradually increased. Similarly, when the maximum relative humidity exceeded 85 (approx.), the oil yield also gradually increased (Figure 5f). Partial dependence plots (PDPs) were used to show how one or two variables affect the predictions of the model. In this study, a univariate partial dependence plot is displayed alongside each pairwise partial dependence plot in a matrix-style layout. An analyst can easily observe and identify the important pairs of variables that have a significant impact on the model using this partial dependence plot.

Mutual Effect of Two Predictors on Response
Partial dependence plots with two variables show the mutual contribution of the variables to the response. Such plots help identify the optimum range of predictor values for a maximum value of the response. Two-variable PDPs (Figures 7a-8e)    In the case of all previous PDPs (single-variable and double-variable PDPs), the variation in oil content is represented as a gradual change in colour intensities. From these plots, it is notably challenging to conclude the precise value ranges of variables in which the response shows variation. In such situations, faceted heatmaps are found to be advantageous. A faceted heatmap for all predictors is provided in Figure 9. The colour scale shows the weight or influence of the predictor on response. The x-axis shows the instances. The y-axis shows the predictor value ranges. The dark blue colour represents positive feature weight, i.e., the variable range supports the response and the dark red color represents negative feature weight [46]. In the plot, the total altitude range is divided into four different groups: ≤60 kg/ha, between 60 kg/ha and 181 kg/ha, 181 to 556 kg/ha, and greater than 556 kg/ha. The first group, ≤60 kg/ha, has a mild negative feature weight; the third group, 181 to 556 kg/ha, has a mild positive feature weight. Average rainfall higher than 6.25 mm has a strong negative effect on basil oil content. The oil content is favoured when the potassium content is less than or equal to 205 kg/ha. Potassium content greater than 603 kg/ha is found to have a negative effect on the oil content. The oil content is found to be positively affected when the maximum relative humidity value is beyond 85.8. The feature weights for other variables are provided in Figure 9. Large multivariate data sets can be explored using heat maps. Color gradients or colour schemes are used to illustrate response variables. The right transformation along with row and column grouping might reveal interesting patterns in the data. In addition, they can be used to display the outcomes of statistical analysis, such as the variables that differ between treatment groups. (a) 3D partial dependence plot for soil potassium content, altitude, and essential oil content; (b) 3D partial dependence plot for soil potassium content, average rainfall, and essential oil content; (c) 3D partial dependence plot for soil potassium content, average temperature, and essential oil content; (d) 3D partial dependence plot for soil potassium content, average maximum relative humidity, and essential oil content (e) 3D partial dependence plot for soil potassium content, pH, and essential oil content.
In the case of all previous PDPs (single-variable and double-variable PDPs), the variation in oil content is represented as a gradual change in colour intensities. From these plots, it is notably challenging to conclude the precise value ranges of variables in which the response shows variation. In such situations, faceted heatmaps are found to be advan- Figure 8. (a) 3D partial dependence plot for soil potassium content, altitude, and essential oil content; (b) 3D partial dependence plot for soil potassium content, average rainfall, and essential oil content; (c) 3D partial dependence plot for soil potassium content, average temperature, and essential oil content; (d) 3D partial dependence plot for soil potassium content, average maximum relative humidity, and essential oil content (e) 3D partial dependence plot for soil potassium content, pH, and essential oil content.
to be positively affected when the maximum relative humidity value is beyond 85.8. The feature weights for other variables are provided in Figure 9. Large multivariate data sets can be explored using heat maps. Color gradients or colour schemes are used to illustrate response variables. The right transformation along with row and column grouping might reveal interesting patterns in the data. In addition, they can be used to display the outcomes of statistical analysis, such as the variables that differ between treatment groups. To the best of our knowledge, this research is the first to look into the impact of soil nutritional parameters and environmental factors on Thai basil oil content in 10 different agroclimatic regions of Odisha. According to this study, a combination of either two factors or more than two factors have greater impacts than a single factor on Thai basil oil content. It was found that adjusting parameters such as height, temperature, rainfall, humidity, and potassium content in the soil can maximize the oil content of Thai basil. The difference in oil content in different regions is influenced by various parameters that need to be further investigated. The prediction model developed in this study will be beneficial to obtain Thai basil oil content in a new site that is close to the experimental values. To the best of our knowledge, this research is the first to look into the impact of soil nutritional parameters and environmental factors on Thai basil oil content in 10 different agroclimatic regions of Odisha. According to this study, a combination of either two factors or more than two factors have greater impacts than a single factor on Thai basil oil content. It was found that adjusting parameters such as height, temperature, rainfall, humidity, and potassium content in the soil can maximize the oil content of Thai basil. The difference in oil content in different regions is influenced by various parameters that need to be further investigated. The prediction model developed in this study will be beneficial to obtain Thai basil oil content in a new site that is close to the experimental values.

Conclusions
To obtain the highest oil yield from Thai basil, it will be advantageous to use the artificial neural network (ANN) model developed to investigate the effect of various environmental factors and soil parameters on oil content. The outcome showed that by developing an ANN, we can forecast the oil concentration at a new site using a combination of environmental and soil data. By modifying the model's input parameters (height, temperature, rainfall, humidity, and potassium), it is possible to increase the oil content of Thai basil. The ANN model is therefore highly helpful to predict and optimize the oil content of Thai basil at a particular site for commercial cultivation. Data Availability Statement: The study did not report any data.